from numpy.lib.function_base import gradient
import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis].astype("float32")
x_test = x_test[..., tf.newaxis].astype("float32")

train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).batch(32)

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation = 'relu')
        self.flatten = Flatten()
        self.d1 = Dense(128, activation = 'relu')
        self.d2 = Dense(10)
        
    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)
    
# Create an instance of the model
model = MyModel()

loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True)
optimizer = tf.keras.optimizers.Adam()

train_loss = tf.keras.metrics.Mean(name = "train_loss")
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        # training=True is only needed if there are layers with different
        # behavior during training versus inference (e.g. Dropout).
        predictions = model(images, training = True)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
    train_loss(loss)
    train_accuracy(labels, predictions)
    
@tf.function
def test_step(images, labels):
  # training=False is only needed if there are layers with different
  # behavior during training versus inference (e.g. Dropout).
    predictions = model(images, training = False)
    t_loss = loss_object(labels, predictions)
    
    test_loss(t_loss)
    test_accuracy(labels, predictions)

Epochs = 5

for epoch in range(Epochs):
  # Reset the metrics at the start of the next epoch
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()
    
    for images, labels in train_ds:
        train_step(images, labels)
        
    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)
        
    print(
        f'Epoch {epoch + 1}, '
        f'Loss: {train_loss.result()}, '
        f'Accuracy: {train_accuracy.result() * 100}, '
        f'Test Loss: {test_loss.result()}, '
        f'Test Accuracy: {test_accuracy.result() * 100}'
    )